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相关概念视频

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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相关实验视频

Updated: Sep 18, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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DeepPSA:用于PROTAC合成可访问性预测的几何深度学习模型

Ran Zhang1, Shihang Wang1, Lin Wang1,2

  • 1Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China.

Journal of chemical information and modeling
|June 25, 2025
PubMed
概括

我们开发了DeepPSA,这是一个深度学习模型,用于预测针对蛋白质溶解的合成仿真体 (PROTACs) 的合成可访问性. 该工具通过评估PROTAC合成可行性,有助于设计新药候选药物.

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相关实验视频

Last Updated: Sep 18, 2025

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13:19

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Published on: March 13, 2021

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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科学领域:

  • 药物的发现和设计.
  • 计算化学是一种计算化学.
  • 医学中的人工智能

背景情况:

  • 化向化体 (PROTACs) 是一种诱导蛋白降解的新疗法.
  • PROTAC的合成是复杂的,阻碍了药物开发.
  • 现有的AI模型缺乏用于评估PROTAC合成可访问性的工具.

研究的目的:

  • 开发一个计算模型来预测PROTAC合成可访问性.
  • 为评估PROTAC合成可行性提供数据驱动的工具.
  • 帮助设计和选新的PROTAC化合物.

主要方法:

  • 开发了DeepPSA,这是一个基于图形的模型,使用图形神经网络架构.
  • 在3644个PROTAC的内部数据集和实验合成数据上训练模型.
  • 在测试和分区数据集上使用预测准确度和AUROC评估模型性能.

主要成果:

  • 在测试组中,DeepPSA实现了92.9%的预测准确度和0.963的AUROC.
  • 该模型在基于结构的分区数据集上展示了卓越的性能和概括能力.
  • DeepPSA 是第一个专门专注于 PROTAC 合成可访问性的模型.

结论:

  • DeepPSA提供了一种可靠和系统的方法来评估PROTAC合成可访问性.
  • 该模型促进了新型PROTACs的高效设计和选.
  • 为了更广泛的使用,DeepPSA可以通过Web服务器和GitHub存储库访问.